132 related articles for article (PubMed ID: 36572323)
1. Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI.
Vats N; Sengupta A; Gupta RK; Patir R; Vaishya S; Ahlawat S; Saini J; Agarwal S; Singh A
Magn Reson Imaging; 2023 May; 98():76-82. PubMed ID: 36572323
[TBL] [Abstract][Full Text] [Related]
2. Glioma grading using a machine-learning framework based on optimized features obtained from T
Sengupta A; Ramaniharan AK; Gupta RK; Agarwal S; Singh A
J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
[TBL] [Abstract][Full Text] [Related]
3. On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.
Sengupta A; Agarwal S; Gupta PK; Ahlawat S; Patir R; Gupta RK; Singh A
Eur J Radiol; 2018 Sep; 106():199-208. PubMed ID: 30150045
[TBL] [Abstract][Full Text] [Related]
4. Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma.
Suhail Parvaze ; Bhattacharjee R; Singh A; Ahlawat S; Patir R; Vaishya S; Shah TJ; Gupta RK
Eur J Radiol; 2023 Feb; 159():110655. PubMed ID: 36577183
[TBL] [Abstract][Full Text] [Related]
5. Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI.
Gupta M; Gupta A; Yadav V; Parvaze SP; Singh A; Saini J; Patir R; Vaishya S; Ahlawat S; Gupta RK
Neuroradiology; 2021 Aug; 63(8):1227-1239. PubMed ID: 33469693
[TBL] [Abstract][Full Text] [Related]
6. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis.
Artzi M; Bressler I; Ben Bashat D
J Magn Reson Imaging; 2019 Aug; 50(2):519-528. PubMed ID: 30635952
[TBL] [Abstract][Full Text] [Related]
7. Differentiation of pilocytic and pilomyxoid astrocytomas using dynamic susceptibility contrast perfusion and diffusion weighted imaging.
Ho CY; Supakul N; Patel PU; Seit V; Groswald M; Cardinal J; Lin C; Kralik SF
Neuroradiology; 2020 Jan; 62(1):81-88. PubMed ID: 31676961
[TBL] [Abstract][Full Text] [Related]
8. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
Shrot S; Salhov M; Dvorski N; Konen E; Averbuch A; Hoffmann C
Neuroradiology; 2019 Jul; 61(7):757-765. PubMed ID: 30949746
[TBL] [Abstract][Full Text] [Related]
9. Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis.
Parvaze PS; Bhattacharjee R; Verma YK; Singh RK; Yadav V; Singh A; Khanna G; Ahlawat S; Trivedi R; Patir R; Vaishya S; Shah TJ; Gupta RK
NMR Biomed; 2023 May; 36(5):e4884. PubMed ID: 36453877
[TBL] [Abstract][Full Text] [Related]
10. Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features.
Dong F; Li Q; Xu D; Xiu W; Zeng Q; Zhu X; Xu F; Jiang B; Zhang M
Eur Radiol; 2019 Aug; 29(8):3968-3975. PubMed ID: 30421019
[TBL] [Abstract][Full Text] [Related]
11. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI.
Kim Y; Cho HH; Kim ST; Park H; Nam D; Kong DS
Neuroradiology; 2018 Dec; 60(12):1297-1305. PubMed ID: 30232517
[TBL] [Abstract][Full Text] [Related]
12. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
Suh HB; Choi YS; Bae S; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
[TBL] [Abstract][Full Text] [Related]
13. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Patel M; Zhan J; Natarajan K; Flintham R; Davies N; Sanghera P; Grist J; Duddalwar V; Peet A; Sawlani V
Clin Radiol; 2021 Aug; 76(8):628.e17-628.e27. PubMed ID: 33941364
[TBL] [Abstract][Full Text] [Related]
14. Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and susceptibility-weighted MRI.
Saini J; Kumar Gupta P; Awasthi A; Pandey CM; Singh A; Patir R; Ahlawat S; Sadashiva N; Mahadevan A; Kumar Gupta R
Clin Radiol; 2018 Nov; 73(11):986.e7-986.e15. PubMed ID: 30197047
[TBL] [Abstract][Full Text] [Related]
15. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.
Nakagawa M; Nakaura T; Namimoto T; Kitajima M; Uetani H; Tateishi M; Oda S; Utsunomiya D; Makino K; Nakamura H; Mukasa A; Hirai T; Yamashita Y
Eur J Radiol; 2018 Nov; 108():147-154. PubMed ID: 30396648
[TBL] [Abstract][Full Text] [Related]
16. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T
Sun YZ; Yan LF; Han Y; Nan HY; Xiao G; Tian Q; Pu WH; Li ZY; Wei XC; Wang W; Cui GB
BMC Med Imaging; 2021 Feb; 21(1):17. PubMed ID: 33535988
[TBL] [Abstract][Full Text] [Related]
17. Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma.
Lin X; Lee M; Buck O; Woo KM; Zhang Z; Hatzoglou V; Omuro A; Arevalo-Perez J; Thomas AA; Huse J; Peck K; Holodny AI; Young RJ
AJNR Am J Neuroradiol; 2017 Mar; 38(3):485-491. PubMed ID: 27932505
[TBL] [Abstract][Full Text] [Related]
18. An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.
Tateishi M; Nakaura T; Kitajima M; Uetani H; Nakagawa M; Inoue T; Kuroda JI; Mukasa A; Yamashita Y
J Neurol Sci; 2020 Mar; 410():116514. PubMed ID: 31869660
[TBL] [Abstract][Full Text] [Related]
19. Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.
Kunimatsu A; Kunimatsu N; Yasaka K; Akai H; Kamiya K; Watadani T; Mori H; Abe O
Magn Reson Med Sci; 2019 Jan; 18(1):44-52. PubMed ID: 29769456
[TBL] [Abstract][Full Text] [Related]
20. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.
Priya S; Liu Y; Ward C; Le NH; Soni N; Pillenahalli Maheshwarappa R; Monga V; Zhang H; Sonka M; Bathla G
Sci Rep; 2021 May; 11(1):10478. PubMed ID: 34006893
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
